Communications engineering最新文献

Offshore wind energy plays a vital role in addressing global energy challenges. Its true value emerges when integrated into holistic systems combining offshore wind farms with coastal power plants, energy storage, and marine ranches. Using East China as a case study, we develop and optimize such clusters to reduce construction and operation costs. Our results show that these clusters significantly enhance energy storage utilization, increase offshore wind absorption such that approximately 20% of wind farms achieve an annual generation absorption rate exceeding 95%, and improve frequency regulation of large power units, achieving an optimized storage configuration of 0.67 GWh. By employing fish cages as flexible loads, the regional absorption rate rises above 98%, generating economic benefits of 6.82 billion RMB annually. Marine ranches also provide 35 kilotons of high-quality protein, advancing food security. These findings highlight the transformative potential of integrating offshore wind into dynamic systems, redefining the interplay between renewable generation, storage, and ancillary services for the East China case. By unlocking the combined benefits of energy and marine resource synergies, this work lays the groundwork for sustainable energy innovation and sustainable development. Its applicability can extend to other regions, provided that local policies and biophysical conditions are met.

Rock thin-section image analysis is a fundamental task in geological and mineralogical research. Traditional methods rely on visual inspection by experts using optical microscopes, which are inherently subjective, experience-dependent, and time-consuming. Here, we introduce RoImAI, a vision foundation model specifically designed for rock thin-section microscopy images, enabling rapid and precise rock segmentation, identification, and lithology report generation. A large-scale dataset of rock thin-section microscopy images comprising 30,336 images and approximately two million rock particles from 17 different regions was constructed to develop and validate RoImAI. RoImAI leverages Transformer-based deep learning techniques to achieve high-precision segmentation across multi-center datasets from diverse geological regions. RoImAI employs a hierarchical classification strategy to identify rock particles accurately. Furthermore, RoImAI outperforms human experts in efficiency and accuracy when generating structured lithology reports. The intelligent analytical capabilities and high accuracy of RoImAI strongly enable the automated processing of the rapidly expanding volume of rock thin-section images.

Medium-chain fatty acids (MCFAs) are carbon-neutral alternative to petroleum-derived chemicals, offering sustainable valorization for waste activated sludge. Current bioproduction systems, however, face a critical dual bottleneck stemming from complex sludge matrices and inefficient carbon flux regulation. Here, we develop a stage-optimized modulation strategy employing alkaline biochar (AlkBC)-ferrate to sequentially enhance sludge solubilization and targeted MCFA-producing fermentation. Maximum MCFA production reaches 10495.0 mg chemical oxygen demand L^-1, 20.6-, 15.2- and 2.3-fold higher than the control, AlkBC-alone and ferrate-alone groups, respectively. Mechanistically, AlkBC initiates ferrate activation to produce metastable Fe(IV)/Fe(V) intermediates via physical adsorption, electron donation, and oxygen-functionalized coordination. High-valent Fe species oxidation coupled with AlkBC-elevated alkalinity efficiently disrupts sludge polymerized structure and drives bioconversion of released organics. Simultaneously, AlkBC is structurally reconfigured due to ferrate oxidation with surface-loaded Fe₂O₃ as active component, substantially enhancing chain elongation. Furthermore, AlkBC-ferrate enriches key functional bacteria while suppressing methanogens, steering carbon flux towards MCFA production.

Coherent Beam Combination (CBC) presents a promising solution to circumvent the power-scaling limitations of High-Power Fiber Lasers (HPFLs) by spatially combining the outputs of multiple independently pumped fibres. This parallel pumping configuration allows each fibre to operate below the critical threshold that would otherwise lead to instability, whilst their combined output exceeds the maximum power achievable from a stably operating HPFL. In this work, we demonstrate that manipulating the relative phases between fibre outputs extends the capabilities of CBC to approximate the phase profiles of optical elements such as spherical lenses, axicon lenses, and spiral phase plates, enabling versatile beam focus shaping and steering. We further show that the combined beam focus, whether coherently combined or shaped into a bespoke profile (e.g. Bessel-like, orbital angular momentum), can be steered and rotated in three-dimensional space through phase-only control. These results are experimentally validated through spatial light modulation to simulate collimated fibre outputs with controllable relative phases. Our findings advance CBC systems beyond mere power scaling, offering pathways for highly versatile beam shaping and steering, with implications for next-generation multifunctional optical power delivery systems.

The nitrogen-vacancy (NV) center is a defect in diamond whose spin state can be read optically by exploiting its photoluminescence or electrically by exploiting its charge generation rate under illumination, both of which being spin-dependent. The latter method offers numerous opportunities in terms of integration and performance compared to conventional optical readout. Here, we investigate the physical properties of a graphitic-diamond-graphitic structure under illumination. We show how, for a type IIa diamond material, electron-hole pairs generated by an ensemble of NV centers lead to a p-type material upon illumination, making this all-carbon structure equivalent to two back-to-back Schottky diodes. We analyze how the reverse current flowing upon illumination changes as a function of bias voltage and radiofrequency-induced excitation of the NV ensemble spin resonances. Furthermore, we demonstrate how an additional field effect arising from the illumination scheme affects the reverse current, resulting in a photoelectrical signal that can exceed the optical signal under the same illumination conditions.

Magnetic Particle Imaging (MPI) is a preclinical imaging modality with potential for future clinical usage. The radiation-free guidance of endovascular interventions with MPI is especially promising. Here, we present a safety study on the heating of metallic medical implants during MPI measurements under realistic conditions in an extracorporeally-perfused cadaver model. The measurements were conducted by fiberoptic thermometers and showed no detectable heating of the tested endovascular devices in the cadaver model. A temperature increase of no more than 0.11 K was observed on the surface of the investigated proximal femoral nail. The in vitro testing of orthopedic prostheses (knee and hip) revealed a slight heating effect of 0.45 K. The dependence of heating on the applied excitation frequency was measured. Overall, the tested repertoire of implants did not heat by a clinically-relevant amount in a human-sized MPI-scanner under realistic conditions, indicating their safe usage in future clinical applications.

Radiation workers need accurate monitoring of X-ray exposure, but existing solutions are either inaccessible, expensive, or provide delayed feedback. We present OpenDosimeter ( www.opendosimeter.org ), an open hardware solution for real-time personal X-ray dose monitoring. Using a scintillator-based X-ray sensor on a custom board powered by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data logging (10 hours), and battery-powered operation. A core innovation is that we calibrate the device using ²⁴¹Am found in ionization smoke detectors. Specifically, we use the γ-emissions to spectrally calibrate the dosimeter, then calculate the effective dose from X-ray exposure using the scintillator absorption efficiency and energy-to-dose coefficients derived from public tabulated data. We demonstrate that this transparent approach enables dose rate readings with linear response between 0.1-1000 μSv/h at  ± 25% accuracy, tested for energies up to 120 keV. The maximum dose rate readings are limited by pile-up effects when approaching count rate saturation (~77,000 counts per second at  ~13 μs average pulse-processing time). The total cost for making an OpenDosimeter is <$100, which, combined with its open design, enables cost-effective local reproducibility on a global scale. This paper complements the open-source documentation by explaining the underlying technology, algorithms, and areas for future improvement.

Electromagnetic waveguides are widely used in spacecraft, naval, electrical, and communication systems for transferring microwave energy. Conventional designs are typically rigid and bulky, offering little shape adaptability. This limits their use in confined spaces or systems requiring compact storage and high adaptability. Here we report highly shape-morphable origami electromagnetic waveguides that fold, deploy, and change shape. Inspired by origami folding techniques, including shopping bags and bellows designs, these waveguides demonstrate low-loss, robust microwave energy transmission in numerical and experimental studies. Results demonstrate that highly shape-morphable electromagnetic waveguides may effectively replace rigid counterparts including straight, twisted, and bent waveguides. This research employs a combined analytical and experimental framework to study the kinematics and mechanics of key geometries, particularly rectangular straight and twist bellows designs, offering rigorous structural design guidance. Engineering advancements and fundamental studies in this work lay the foundation for future adaptive microwave energy delivery waveguides.

Conventional dry filtration materials present a high initial efficiency but suffer from poor regenerability and limited functionality. Conversely, liquid-based purification strategies leverage the high capture potential of gas-liquid interfaces, but face challenges such as insufficient material strength, complex fabrication, and difficulties in large-scale application. Here, we proposed a porous ceramic-based bubble-enhanced filtration system (PCBEFS). The proposed system employs tunable porous ceramics as bubble generators in the water medium, thereby enabling highly efficient wet removal of particulate matter, while simultaneously providing air humidification, formaldehyde removal, and antibacterial functions. Furthermore, we established an empirical mathematical model linking pore structure, bubble characteristics, and removal performance, which elucidates the structure-activity relationship and presents insights into the capture mechanism jointly governed by porous ceramics and gas-liquid interfaces. PCBEFS addresses the limitations of the existing liquid-based purification systems and contributes substantially to multifunctional indoor air pollution control with strong engineering potential.

Event-based imaging is at the forefront of high-speed sensing applications due to the low latency of asynchronous detection and low data volume. Conventionally, events used in this form of sensing correspond to changes in intensity on the microsecond scale, this inherently makes the approach incompatible with scenarios where single photon detection is required such as single photon Light Detection and Ranging (LiDAR), and low-light-level imaging. Here we propose a new imaging modality which is driven instead by events generated from the detection of individual photons. We use a form of single-pixel imaging in which information from a 3-Dimensional scene is encoded entirely in the time-of-arrival of the photons such that each detected photon can be used to update an estimation of all transverse positions simultaneously. The image reconstruction is performed by a Spiking Convolutional Neural Network (SCNN) which has a natural complementarity with single photon detection that allows the scheme to run fully asynchronously and be driven by the detection of each individual photon. Our approach for processing LiDAR information asynchronously, driven by each detected photon, has the potential for minimal latency 3D imaging and sensing even in weak light conditions with applications in high speed target detection and robotics.